Docstring check (#26052)

* Fix number of minimal calls to the Hub with peft integration

* Alternate design

* And this way?

* Revert

* Nits to fix

* Add util

* Print when changes are made

* Add list to ignore

* Add more rules

* Manual fixes

* deal with kwargs

* deal with enum defaults

* avoid many digits for floats

* Manual fixes

* Fix regex

* Fix regex

* Auto fix

* Style

* Apply script

* Add ignored list

* Add check that templates are filled

* Adding to CI checks

* Add back semi-fix

* Ignore more objects

* More auto-fixes

* Ignore missing objects

* Remove temp semi-fix

* Fixes

* Update src/transformers/models/pvt/configuration_pvt.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update utils/check_docstrings.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/utils/quantization_config.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Deal with float defaults

* Fix small defaults

* Address review comment

* Treat

* Post-rebase cleanup

* Address review comment

* Update src/transformers/models/deprecated/mctct/configuration_mctct.py

Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

* Address review comment

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

.circleci/config.yml

@@ -209,6 +209,7 @@ jobs:
             - run: make deps_table_check_updated
             - run: python utils/update_metadata.py --check-only
             - run: python utils/check_task_guides.py
+            - run: python utils/check_docstrings.py
 
 workflows:
     version: 2

Makefile

@@ -43,6 +43,7 @@ repo-consistency:
 	python utils/check_doctest_list.py
 	python utils/update_metadata.py --check-only
 	python utils/check_task_guides.py
+	python utils/check_docstrings.py
 
 # this target runs checks on all files
 
@@ -82,6 +83,7 @@ fix-copies:
 	python utils/check_dummies.py --fix_and_overwrite
 	python utils/check_doctest_list.py --fix_and_overwrite
 	python utils/check_task_guides.py --fix_and_overwrite
+	python utils/check_docstrings.py --fix_and_overwrite
 
 # Run tests for the library
 

docs/source/en/pr_checks.md

@@ -124,6 +124,7 @@ This checks that:
 - The translations of the READMEs and the index of the doc have the same model list as the main README (performed by `utils/check_copies.py`)
 - The auto-generated tables in the documentation are up to date (performed by `utils/check_table.py`)
 - The library has all objects available even if not all optional dependencies are installed (performed by `utils/check_dummies.py`)
+- All docstrings properly document the arguments in the signature of the object (performed by `utils/check_docstrings.py`)
 
 Should this check fail, the first two items require manual fixing, the last four can be fixed automatically for you by running the command
 

src/transformers/configuration_utils.py

@@ -47,6 +47,7 @@ _re_configuration_file = re.compile(r"config\.(.*)\.json")
 
 
 class PretrainedConfig(PushToHubMixin):
+    # no-format
     r"""
     Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
     methods for loading/downloading/saving configurations.

src/transformers/data/data_collator.py

@@ -90,7 +90,7 @@ class DefaultDataCollator(DataCollatorMixin):
     helpful if you need to set a return_tensors value at initialization.
 
     Args:
-        return_tensors (`str`):
+        return_tensors (`str`, *optional*, defaults to `"pt"`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
     """
 
@@ -235,7 +235,7 @@ class DataCollatorWithPadding:
 
             This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
             7.5 (Volta).
-        return_tensors (`str`):
+        return_tensors (`str`, *optional*, defaults to `"pt"`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
     """
 
@@ -288,7 +288,7 @@ class DataCollatorForTokenClassification(DataCollatorMixin):
             7.5 (Volta).
         label_pad_token_id (`int`, *optional*, defaults to -100):
             The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
-        return_tensors (`str`):
+        return_tensors (`str`, *optional*, defaults to `"pt"`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
     """
 
@@ -521,7 +521,7 @@ class DataCollatorForSeq2Seq:
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        model ([`PreTrainedModel`]):
+        model ([`PreTrainedModel`], *optional*):
             The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
             prepare the *decoder_input_ids*
 
@@ -544,7 +544,7 @@ class DataCollatorForSeq2Seq:
             7.5 (Volta).
         label_pad_token_id (`int`, *optional*, defaults to -100):
             The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
-        return_tensors (`str`):
+        return_tensors (`str`, *optional*, defaults to `"pt"`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
     """
 

src/transformers/feature_extraction_utils.py

@@ -65,7 +65,7 @@ class BatchFeature(UserDict):
     This class is derived from a python dictionary and can be used as a dictionary.
 
     Args:
-        data (`dict`):
+        data (`dict`, *optional*):
             Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask',
             etc.).
         tensor_type (`Union[None, str, TensorType]`, *optional*):

src/transformers/generation/beam_constraints.py

@@ -263,8 +263,9 @@ class DisjunctiveConstraint(Constraint):
     A special [`Constraint`] that is fulfilled by fulfilling just one of several constraints.
 
     Args:
-        nested_token_ids (`List[List[int]]`): a list of words, where each word is a list of ids. This constraint
+        nested_token_ids (`List[List[int]]`):
-        is fulfilled by generating just one from the list of words.
+            A list of words, where each word is a list of ids. This constraint is fulfilled by generating just one from
+            the list of words.
     """
 
     def __init__(self, nested_token_ids: List[List[int]]):

src/transformers/generation/beam_search.py

@@ -152,7 +152,7 @@ class BeamSearchScorer(BeamScorer):
         num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
             The number of beam hypotheses that shall be returned upon calling
             [`~transformer.BeamSearchScorer.finalize`].
-        num_beam_groups (`int`):
+        num_beam_groups (`int`, *optional*, defaults to 1):
             Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
             See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
         max_length (`int`, *optional*):
@@ -437,7 +437,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
         num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
             The number of beam hypotheses that shall be returned upon calling
             [`~transformer.BeamSearchScorer.finalize`].
-        num_beam_groups (`int`):
+        num_beam_groups (`int`, *optional*, defaults to 1):
             Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
             See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
         max_length (`int`, *optional*):

src/transformers/generation/configuration_utils.py

@@ -38,6 +38,7 @@ METADATA_FIELDS = ("_from_model_config", "_commit_hash", "_original_object_hash"
 
 
 class GenerationConfig(PushToHubMixin):
+    # no-format
     r"""
     Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
     for text-decoder, text-to-text, speech-to-text, and vision-to-text models:

src/transformers/generation/flax_logits_process.py

@@ -120,7 +120,7 @@ class FlaxTopPLogitsWarper(FlaxLogitsWarper):
         top_p (`float`):
             If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
             higher are kept for generation.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -163,7 +163,7 @@ class FlaxTopKLogitsWarper(FlaxLogitsWarper):
     Args:
         top_k (`int`):
             The number of highest probability vocabulary tokens to keep for top-k-filtering.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.

src/transformers/generation/logits_process.py

@@ -357,7 +357,7 @@ class TopPLogitsWarper(LogitsWarper):
         top_p (`float`):
             If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
             higher are kept for generation.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -419,7 +419,7 @@ class TopKLogitsWarper(LogitsWarper):
     Args:
         top_k (`int`):
             The number of highest probability vocabulary tokens to keep for top-k-filtering.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -447,9 +447,9 @@ class TypicalLogitsWarper(LogitsWarper):
     Generation](https://arxiv.org/abs/2202.00666) for more information.
 
     Args:
-        mass (`float`):
+        mass (`float`, *optional*, defaults to 0.9):
             Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -499,7 +499,7 @@ class EpsilonLogitsWarper(LogitsWarper):
     Args:
         epsilon (`float`):
             If set to > 0, only the most tokens with probabilities `epsilon` or higher are kept for generation.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -572,7 +572,7 @@ class EtaLogitsWarper(LogitsWarper):
         epsilon (`float`):
             A float value in the range (0, 1). Hyperparameter used to calculate the dynamic cutoff value, `eta`. The
             suggested values from the paper ranges from 3e-4 to 4e-3 depending on the size of the model.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All values that are found to be below the dynamic cutoff value, `eta`, are set to this float value. This
             parameter is useful when logits need to be modified for very low probability tokens that should be excluded
             from generation entirely.
@@ -1600,18 +1600,15 @@ class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
             Higher guidance scale encourages the model to generate samples that are more closely linked to the input
             prompt, usually at the expense of poorer quality. A value smaller than 1 has the opposite effect, while
             making the negative prompt provided with negative_prompt_ids (if any) act as a positive prompt.
-        unconditional_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of input sequence tokens in the vocabulary for the unconditional branch. If unset, will default to
-            the last token of the prompt.
-        unconditional_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, **optional**):
-            Attention mask for unconditional_ids.
         model (`PreTrainedModel`):
             The model computing the unconditional scores. Supposedly the same as the one computing the conditional
             scores. Both models must use the same tokenizer.
-        smooth_factor (`float`, **optional**):
+        unconditional_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            The interpolation weight for CFG Rescale. 1 means no rescaling, 0 reduces to the conditional scores without
+            Indices of input sequence tokens in the vocabulary for the unconditional branch. If unset, will default to
-            CFG. Turn it lower if the output degenerates.
+            the last token of the prompt.
-        use_cache (`bool`, **optional**):
+        unconditional_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Attention mask for unconditional_ids.
+        use_cache (`bool`, *optional*, defaults to `True`):
             Whether to cache key/values during the negative prompt forward pass.
 
 

src/transformers/generation/stopping_criteria.py

@@ -49,7 +49,7 @@ class MaxLengthCriteria(StoppingCriteria):
     Args:
         max_length (`int`):
             The maximum length that the output sequence can have in number of tokens.
-        max_position_embeddings (`int`, `optional`):
+        max_position_embeddings (`int`, *optional*):
             The maximum model length, as defined by the model's `config.max_position_embeddings` attribute.
     """
 

src/transformers/generation/tf_logits_process.py

@@ -122,7 +122,7 @@ class TFTopKLogitsWarper(TFLogitsWarper):
     Args:
         top_k (`int`):
             The number of highest probability vocabulary tokens to keep for top-k-filtering.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.
@@ -151,7 +151,7 @@ class TFTopPLogitsWarper(TFLogitsWarper):
         top_p (`float`):
             If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
             higher are kept for generation.
-        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+        filter_value (`float`, *optional*, defaults to -inf):
             All filtered values will be set to this float value.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
             Minimum number of tokens that cannot be filtered.

src/transformers/models/align/configuration_align.py

@@ -71,6 +71,8 @@ class AlignTextConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
         position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
             Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
             positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
@@ -80,8 +82,6 @@ class AlignTextConfig(PretrainedConfig):
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the model should return the last key/values attentions (not used by all models). Only
             relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*, defaults to 0)
-            Padding token id.
 
     Example:
 

src/transformers/models/altclip/configuration_altclip.py

@@ -259,7 +259,7 @@ class AltCLIPConfig(PretrainedConfig):
             Dictionary of configuration options used to initialize [`AltCLIPTextConfig`].
         vision_config (`dict`, *optional*):
             Dictionary of configuration options used to initialize [`AltCLIPVisionConfig`].
-        projection_dim (`int`, *optional*, defaults to 512):
+        projection_dim (`int`, *optional*, defaults to 768):
             Dimentionality of text and vision projection layers.
         logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
             The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation.

src/transformers/models/altclip/processing_altclip.py

@@ -30,9 +30,9 @@ class AltCLIPProcessor(ProcessorMixin):
     the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`CLIPImageProcessor`]):
+        image_processor ([`CLIPImageProcessor`], *optional*):
             The image processor is a required input.
-        tokenizer ([`XLMRobertaTokenizerFast`]):
+        tokenizer ([`XLMRobertaTokenizerFast`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py

@@ -51,15 +51,15 @@ class ASTConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        patch_size (`int`, *optional*, defaults to `16`):
+        patch_size (`int`, *optional*, defaults to 16):
             The size (resolution) of each patch.
         qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether to add a bias to the queries, keys and values.

src/transformers/models/bark/processing_bark.py

@@ -38,7 +38,7 @@ class BarkProcessor(ProcessorMixin):
     Args:
         tokenizer ([`PreTrainedTokenizer`]):
             An instance of [`PreTrainedTokenizer`].
-        speaker_embeddings (`Dict[Dict[str]]`, *optional*, defaults to `None`):
+        speaker_embeddings (`Dict[Dict[str]]`, *optional*):
             Optional nested speaker embeddings dictionary. The first level contains voice preset names (e.g
             `"en_speaker_4"`). The second level contains `"semantic_prompt"`, `"coarse_prompt"` and `"fine_prompt"`
             embeddings. The values correspond to the path of the corresponding `np.ndarray`. See

src/transformers/models/barthez/tokenization_barthez.py

@@ -97,8 +97,6 @@ class BarthezTokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<mask>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
             SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,

src/transformers/models/bartpho/tokenization_bartpho.py

@@ -92,8 +92,6 @@ class BartphoTokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<mask>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
             SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,

src/transformers/models/beit/configuration_beit.py

@@ -41,7 +41,7 @@ class BeitConfig(PretrainedConfig):
     [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) architecture.
 
     Args:
-        vocab_size (`int`, *optional*, defaults to 8092):
+        vocab_size (`int`, *optional*, defaults to 8192):
             Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
             pre-training.
         hidden_size (`int`, *optional*, defaults to 768):

src/transformers/models/beit/image_processing_beit.py

@@ -57,7 +57,7 @@ class BeitImageProcessor(BaseImageProcessor):
         size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
             Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
             method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
             `preprocess` method.
         do_center_crop (`bool`, *optional*, defaults to `True`):
@@ -67,12 +67,12 @@ class BeitImageProcessor(BaseImageProcessor):
         crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
             Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
             Can be overridden by the `crop_size` parameter in the `preprocess` method.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
-            parameter in the `preprocess` method.
         rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
             Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
             `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
             method.

src/transformers/models/bertweet/tokenization_bertweet.py

@@ -77,7 +77,7 @@ class BertweetTokenizer(PreTrainedTokenizer):
             Path to the vocabulary file.
         merges_file (`str`):
             Path to the merges file.
-        normalization (`bool`, *optional*, defaults to `False`)
+        normalization (`bool`, *optional*, defaults to `False`):
             Whether or not to apply a normalization preprocess.
         bos_token (`str`, *optional*, defaults to `"<s>"`):
             The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

src/transformers/models/big_bird/tokenization_big_bird.py

@@ -60,25 +60,25 @@ class BigBirdTokenizer(PreTrainedTokenizer):
         vocab_file (`str`):
             [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
             contains the vocabulary necessary to instantiate a tokenizer.
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The begin of sequence token.
         unk_token (`str`, *optional*, defaults to `"<unk>"`):
             The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
             token instead.
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
+            The begin of sequence token.
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
         pad_token (`str`, *optional*, defaults to `"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
         sep_token (`str`, *optional*, defaults to `"[SEP]"`):
             The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
             sequence classification or for a text and a question for question answering. It is also used as the last
             token of a sequence built with special tokens.
-        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
         mask_token (`str`, *optional*, defaults to `"[MASK]"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
+        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
             SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,

src/transformers/models/biogpt/configuration_biogpt.py

@@ -72,13 +72,14 @@ class BioGptConfig(PretrainedConfig):
             Please refer to the paper about LayerDrop: https://arxiv.org/abs/1909.11556 for further details
         activation_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for activations inside the fully connected layer.
-        pad_token_id (`int`, *optional*, defaults to 1)
+        pad_token_id (`int`, *optional*, defaults to 1):
             Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 0)
+        bos_token_id (`int`, *optional*, defaults to 0):
             Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 2)
+        eos_token_id (`int`, *optional*, defaults to 2):
             End of stream token id.
-        Example:
+
+    Example:
 
     ```python
     >>> from transformers import BioGptModel, BioGptConfig

src/transformers/models/bit/configuration_bit.py

@@ -52,7 +52,7 @@ class BitConfig(BackboneConfigMixin, PretrainedConfig):
             are supported.
         global_padding (`str`, *optional*):
             Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
-        num_groups (`int`, *optional*, defaults to `32`):
+        num_groups (`int`, *optional*, defaults to 32):
             Number of groups used for the `BitGroupNormActivation` layers.
         drop_path_rate (`float`, *optional*, defaults to 0.0):
             The drop path rate for the stochastic depth.

src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py

@@ -85,9 +85,9 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
         unk_token (`str`, *optional*, defaults to `"__unk__"`):
             The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
             token instead.
-        pad_token (`str`, *optional*, defaults to `"__pad__"`):
+        pad_token (`str`, *optional*, defaults to `"__null__"`):
             The token used for padding, for example when batching sequences of different lengths.
-        **kwargs
+        kwargs (*optional*):
             Additional keyword arguments passed along to [`PreTrainedTokenizer`]
     """
 

src/transformers/models/blip/configuration_blip.py

@@ -295,7 +295,7 @@ class BlipConfig(PretrainedConfig):
             Dimentionality of text and vision projection layers.
         logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
             The inital value of the *logit_scale* paramter. Default is used as per the original BLIP implementation.
-        image_text_hidden_size (`int`, *optional*, defaults to 768):
+        image_text_hidden_size (`int`, *optional*, defaults to 256):
             Dimentionality of the hidden state of the image-text fusion layer.
         kwargs (*optional*):
             Dictionary of keyword arguments.

src/transformers/models/blip/image_processing_blip.py

@@ -53,7 +53,7 @@ class BlipImageProcessor(BaseImageProcessor):
         size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
             Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
             method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
             overridden by the `resample` parameter in the `preprocess` method.
         do_rescale (`bool`, *optional*, defaults to `True`):

src/transformers/models/bridgetower/image_processing_bridgetower.py

@@ -128,14 +128,14 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
         do_resize (`bool`, *optional*, defaults to `True`):
             Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
             `do_resize` parameter in the `preprocess` method.
-        size (`Dict[str, int]` *optional*, defaults to `288`):
+        size (`Dict[str, int]` *optional*, defaults to 288):
             Resize the shorter side of the input to `size["shortest_edge"]`. The longer side will be limited to under
             `int((1333 / 800) * size["shortest_edge"])` while preserving the aspect ratio. Only has an effect if
             `do_resize` is set to `True`. Can be overridden by the `size` parameter in the `preprocess` method.
         size_divisor (`int`, *optional*, defaults to 32):
             The size by which to make sure both the height and width can be divided. Only has an effect if `do_resize`
             is set to `True`. Can be overridden by the `size_divisor` parameter in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
             overridden by the `resample` parameter in the `preprocess` method.
         do_rescale (`bool`, *optional*, defaults to `True`):

src/transformers/models/bros/processing_bros.py

@@ -31,7 +31,7 @@ class BrosProcessor(ProcessorMixin):
     [`~BrosProcessor.__call__`] and [`~BrosProcessor.decode`] for more information.
 
     Args:
-        tokenizer (`BertTokenizerFast`):
+        tokenizer (`BertTokenizerFast`, *optional*):
             An instance of ['BertTokenizerFast`]. The tokenizer is a required input.
     """
     attributes = ["tokenizer"]

src/transformers/models/byt5/tokenization_byt5.py

@@ -48,7 +48,7 @@ class ByT5Tokenizer(PreTrainedTokenizer):
             token instead.
         pad_token (`str`, *optional*, defaults to `"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
-        extra_ids (`int`, *optional*, defaults to 100):
+        extra_ids (`int`, *optional*, defaults to 125):
             Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
             accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
             indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary

src/transformers/models/camembert/tokenization_camembert.py

@@ -89,7 +89,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<mask>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
+        additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED']`):
             Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for

src/transformers/models/chinese_clip/processing_chinese_clip.py

@@ -31,9 +31,9 @@ class ChineseCLIPProcessor(ProcessorMixin):
     See the [`~ChineseCLIPProcessor.__call__`] and [`~ChineseCLIPProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`ChineseCLIPImageProcessor`]):
+        image_processor ([`ChineseCLIPImageProcessor`], *optional*):
             The image processor is a required input.
-        tokenizer ([`BertTokenizerFast`]):
+        tokenizer ([`BertTokenizerFast`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/clap/configuration_clap.py

@@ -227,7 +227,7 @@ class ClapAudioConfig(PretrainedConfig):
         projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
             The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
             `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        layer_norm_eps (`[type]`, *optional*, defaults to `1e-5`):
+        layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         initializer_factor (`float`, *optional*, defaults to 1.0):
             A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
@@ -345,10 +345,10 @@ class ClapConfig(PretrainedConfig):
             Dictionary of configuration options used to initialize [`ClapTextConfig`].
         audio_config (`dict`, *optional*):
             Dictionary of configuration options used to initialize [`ClapAudioConfig`].
+        logit_scale_init_value (`float`, *optional*, defaults to 14.29):
+            The inital value of the *logit_scale* paramter. Default is used as per the original CLAP implementation.
         projection_dim (`int`, *optional*, defaults to 512):
             Dimentionality of text and audio projection layers.
-        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
-            The inital value of the *logit_scale* paramter. Default is used as per the original CLAP implementation.
         projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
             Activation function for the projection layers.
         initializer_factor (`float`, *optional*, defaults to 1.0):

src/transformers/models/clap/feature_extraction_clap.py

@@ -41,32 +41,32 @@ class ClapFeatureExtractor(SequenceFeatureExtractor):
     Fourier Transform* (STFT) which should match pytorch's `torch.stft` equivalent.
 
     Args:
-        feature_size (`int`, defaults to 64):
+        feature_size (`int`, *optional*, defaults to 64):
             The feature dimension of the extracted Mel spectrograms. This corresponds to the number of mel filters
             (`n_mels`).
-        sampling_rate (`int`, defaults to 48_000):
+        sampling_rate (`int`, *optional*, defaults to 48000):
             The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). This only serves
             to warn users if the audio fed to the feature extractor does not have the same sampling rate.
-        hop_length (`int`, defaults to 480):
+        hop_length (`int`,*optional*, defaults to 480):
             Length of the overlaping windows for the STFT used to obtain the Mel Spectrogram. The audio will be split
             in smaller `frames` with a step of `hop_length` between each frame.
-        max_length_s (`int`, defaults to 10):
+        max_length_s (`int`, *optional*, defaults to 10):
             The maximum input length of the model in seconds. This is used to pad the audio.
-        fft_window_size (`int`, defaults to 1024):
+        fft_window_size (`int`, *optional*, defaults to 1024):
             Size of the window (in samples) on which the Fourier transform is applied. This controls the frequency
             resolution of the spectrogram. 400 means that the fourrier transform is computed on windows of 400 samples.
         padding_value (`float`, *optional*, defaults to 0.0):
             Padding value used to pad the audio. Should correspond to silences.
         return_attention_mask (`bool`, *optional*, defaults to `False`):
             Whether or not the model should return the attention masks coresponding to the input.
-        frequency_min (`float`, *optional*, default to 0):
+        frequency_min (`float`, *optional*, defaults to 0):
             The lowest frequency of interest. The STFT will not be computed for values below this.
-        frequency_max (`float`, *optional*, default to 14_000):
+        frequency_max (`float`, *optional*, defaults to 14000):
             The highest frequency of interest. The STFT will not be computed for values above this.
         top_db (`float`, *optional*):
             The highest decibel value used to convert the mel spectrogram to the log scale. For more details see the
             `audio_utils.power_to_db` function
-        truncation (`str`, *optional*, default to `"fusions"`):
+        truncation (`str`, *optional*, defaults to `"fusion"`):
             Truncation pattern for long audio inputs. Two patterns are available:
                 - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and a
                   downsampled version of the entire mel spectrogram.

src/transformers/models/clip/processing_clip.py

@@ -30,9 +30,9 @@ class CLIPProcessor(ProcessorMixin):
     [`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`CLIPImageProcessor`]):
+        image_processor ([`CLIPImageProcessor`], *optional*):
             The image processor is a required input.
-        tokenizer ([`CLIPTokenizerFast`]):
+        tokenizer ([`CLIPTokenizerFast`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/clipseg/configuration_clipseg.py

@@ -255,7 +255,7 @@ class CLIPSegConfig(PretrainedConfig):
             Dimensionality of text and vision projection layers.
         logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
             The inital value of the *logit_scale* paramter. Default is used as per the original CLIPSeg implementation.
-        extract_layers (`List[int]`, *optional*, defaults to [3, 6, 9]):
+        extract_layers (`List[int]`, *optional*, defaults to `[3, 6, 9]`):
             Layers to extract when forwarding the query image through the frozen visual backbone of CLIP.
         reduce_dim (`int`, *optional*, defaults to 64):
             Dimensionality to reduce the CLIP vision embedding.

src/transformers/models/clipseg/processing_clipseg.py

@@ -30,9 +30,9 @@ class CLIPSegProcessor(ProcessorMixin):
     [`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`ViTImageProcessor`]):
+        image_processor ([`ViTImageProcessor`], *optional*):
             The image processor is a required input.
-        tokenizer ([`CLIPTokenizerFast`]):
+        tokenizer ([`CLIPTokenizerFast`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/convnext/image_processing_convnext.py

@@ -64,7 +64,7 @@ class ConvNextImageProcessor(BaseImageProcessor):
         crop_pct (`float` *optional*, defaults to 224 / 256):
             Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be
             overriden by `crop_pct` in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
             Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in

src/transformers/models/cpmant/configuration_cpmant.py

@@ -50,15 +50,17 @@ class CpmAntConfig(PretrainedConfig):
             Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
         num_hidden_layers (`int`, *optional*, defaults to 48):
             Number of layers of the Transformer encoder.
-        dropout_p (`float`, *optional*, defaults to 0.1):
+        dropout_p (`float`, *optional*, defaults to 0.0):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder.
         position_bias_num_buckets (`int`, *optional*, defaults to 512):
             The number of position_bias buckets.
         position_bias_max_distance (`int`, *optional*, defaults to 2048):
             The maximum sequence length that this model might ever be used with. Typically set this to something large
             just in case (e.g., 512 or 1024 or 2048).
-        eps (`float`, *optional*, defaults to 1e-6):
+        eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the layer normalization layers.
+        init_std (`float`, *optional*, defaults to 1.0):
+            Initialize parameters with std = init_std.
         prompt_types (`int`, *optional*, defaults to 32):
             The type of prompt.
         prompt_length (`int`, *optional*, defaults to 32):
@@ -67,8 +69,6 @@ class CpmAntConfig(PretrainedConfig):
             The type of segment.
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether to use cache.
-        init_std (`float`, *optional*, defaults to 1.0):
-            Initialize parameters with std = init_std.
 
     Example:
 

src/transformers/models/ctrl/configuration_ctrl.py

@@ -54,7 +54,7 @@ class CTRLConfig(PretrainedConfig):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
         embd_pdrop (`int`, *optional*, defaults to 0.1):
             The dropout ratio for the embeddings.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-06):
             The epsilon to use in the layer normalization layers
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

src/transformers/models/deberta/tokenization_deberta_fast.py

@@ -99,9 +99,9 @@ class DebertaTokenizerFast(PreTrainedTokenizerFast):
     refer to this superclass for more information regarding those methods.
 
     Args:
-        vocab_file (`str`):
+        vocab_file (`str`, *optional*):
             Path to the vocabulary file.
-        merges_file (`str`):
+        merges_file (`str`, *optional*):
             Path to the merges file.
         tokenizer_file (`str`, *optional*):
             The path to a tokenizer file to use instead of the vocab file.

src/transformers/models/deit/configuration_deit.py

@@ -58,23 +58,23 @@ class DeiTConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        image_size (`int`, *optional*, defaults to `224`):
+        image_size (`int`, *optional*, defaults to 224):
             The size (resolution) of each image.
-        patch_size (`int`, *optional*, defaults to `16`):
+        patch_size (`int`, *optional*, defaults to 16):
             The size (resolution) of each patch.
-        num_channels (`int`, *optional*, defaults to `3`):
+        num_channels (`int`, *optional*, defaults to 3):
             The number of input channels.
         qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether to add a bias to the queries, keys and values.
-        encoder_stride (`int`, `optional`, defaults to 16):
+        encoder_stride (`int`, *optional*, defaults to 16):
             Factor to increase the spatial resolution by in the decoder head for masked image modeling.
 
     Example:

src/transformers/models/deit/image_processing_deit.py

@@ -52,19 +52,19 @@ class DeiTImageProcessor(BaseImageProcessor):
             `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
             Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_center_crop (`bool`, *optional*, defaults to `True`):
             Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
             is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`.
         crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
             Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
-            parameter in the `preprocess` method.
         rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
             Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
             `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
             method.

src/transformers/models/deprecated/mctct/configuration_mctct.py

@@ -53,7 +53,7 @@ class MCTCTConfig(PretrainedConfig):
             Dimensions of each attention head for each attention layer in the Transformer encoder.
         max_position_embeddings (`int`, *optional*, defaults to 920):
             The maximum sequence length that this model might ever be used with (after log-mel spectrogram extraction).
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         layerdrop (`float`, *optional*, defaults to 0.3):
             The probability of dropping an encoder layer during training. The default 0.3 value is used in the original
@@ -63,9 +63,9 @@ class MCTCTConfig(PretrainedConfig):
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.3):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.3):
             The dropout ratio for the attention probabilities.
         pad_token_id (`int`, *optional*, defaults to 1):
             The tokenizer index of the pad token.
@@ -80,17 +80,17 @@ class MCTCTConfig(PretrainedConfig):
             The probability of randomly dropping the `Conv1dSubsampler` layer during training.
         num_conv_layers (`int`, *optional*, defaults to 1):
             Number of convolution layers before applying transformer encoder layers.
-        conv_kernel (`List[int]`, *optional*, defaults to `[7]`):
+        conv_kernel (`Sequence[int]`, *optional*, defaults to `(7,)`):
             The kernel size of the 1D convolution applied before transformer layers. `len(conv_kernel)` must be equal
             to `num_conv_layers`.
-        conv_stride (`List[int]`, *optional*, defaults to `[3]`):
+        conv_stride (`Sequence[int]`, *optional*, defaults to `(3,)`):
             The stride length of the 1D convolution applied before transformer layers. `len(conv_stride)` must be equal
             to `num_conv_layers`.
         input_feat_per_channel (`int`, *optional*, defaults to 80):
             Feature dimensions of the channels of the input to the Conv1D layer.
         input_channels (`int`, *optional*, defaults to 1):
             Number of input channels of the input to the Conv1D layer.
-        conv_channels (`List[int]`, *optional*, defaults to None):
+        conv_channels (`List[int]`, *optional*):
             Channel sizes of intermediate Conv1D layers.
         ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
             Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an

src/transformers/models/deprecated/van/configuration_van.py

@@ -57,9 +57,9 @@ class VanConfig(PretrainedConfig):
             `"selu"` and `"gelu_new"` are supported.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the layer normalization layers.
-        layer_scale_init_value (`float`, *optional*, defaults to 1e-2):
+        layer_scale_init_value (`float`, *optional*, defaults to 0.01):
             The initial value for layer scaling.
         drop_path_rate (`float`, *optional*, defaults to 0.0):
             The dropout probability for stochastic depth.

src/transformers/models/dinat/configuration_dinat.py

@@ -44,9 +44,9 @@ class DinatConfig(BackboneConfigMixin, PretrainedConfig):
             The number of input channels.
         embed_dim (`int`, *optional*, defaults to 64):
             Dimensionality of patch embedding.
-        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
+        depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
             Number of layers in each level of the encoder.
-        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
+        num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
             Number of attention heads in each layer of the Transformer encoder.
         kernel_size (`int`, *optional*, defaults to 7):
             Neighborhood Attention kernel size.
@@ -67,7 +67,7 @@ class DinatConfig(BackboneConfigMixin, PretrainedConfig):
             `"selu"` and `"gelu_new"` are supported.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         layer_scale_init_value (`float`, *optional*, defaults to 0.0):
             The initial value for the layer scale. Disabled if <=0.

src/transformers/models/dinov2/configuration_dinov2.py

@@ -60,7 +60,7 @@ class Dinov2Config(BackboneConfigMixin, PretrainedConfig):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the layer normalization layers.
         image_size (`int`, *optional*, defaults to 224):
             The size (resolution) of each image.

src/transformers/models/donut/configuration_donut_swin.py

@@ -45,15 +45,15 @@ class DonutSwinConfig(PretrainedConfig):
             The number of input channels.
         embed_dim (`int`, *optional*, defaults to 96):
             Dimensionality of patch embedding.
-        depths (`list(int)`, *optional*, defaults to [2, 2, 6, 2]):
+        depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
             Depth of each layer in the Transformer encoder.
-        num_heads (`list(int)`, *optional*, defaults to [3, 6, 12, 24]):
+        num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
             Number of attention heads in each layer of the Transformer encoder.
         window_size (`int`, *optional*, defaults to 7):
             Size of windows.
         mlp_ratio (`float`, *optional*, defaults to 4.0):
             Ratio of MLP hidden dimensionality to embedding dimensionality.
-        qkv_bias (`bool`, *optional*, defaults to True):
+        qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether or not a learnable bias should be added to the queries, keys and values.
         hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probability for all fully connected layers in the embeddings and encoder.
@@ -64,11 +64,11 @@ class DonutSwinConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
             `"selu"` and `"gelu_new"` are supported.
-        use_absolute_embeddings (`bool`, *optional*, defaults to False):
+        use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
             Whether or not to add absolute position embeddings to the patch embeddings.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
 
     Example:

src/transformers/models/donut/processing_donut.py

@@ -32,9 +32,9 @@ class DonutProcessor(ProcessorMixin):
     [`~DonutProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`DonutImageProcessor`]):
+        image_processor ([`DonutImageProcessor`], *optional*):
             An instance of [`DonutImageProcessor`]. The image processor is a required input.
-        tokenizer ([`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]):
+        tokenizer ([`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`], *optional*):
             An instance of [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/dpt/configuration_dpt.py

@@ -52,9 +52,9 @@ class DPTConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -66,6 +66,8 @@ class DPTConfig(PretrainedConfig):
             The size (resolution) of each patch.
         num_channels (`int`, *optional*, defaults to 3):
             The number of input channels.
+        is_hybrid (`bool`, *optional*, defaults to `False`):
+            Whether to use a hybrid backbone. Useful in the context of loading DPT-Hybrid models.
         qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether to add a bias to the queries, keys and values.
         backbone_out_indices (`List[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
@@ -79,11 +81,9 @@ class DPTConfig(PretrainedConfig):
             - "project" passes information to the other tokens by concatenating the readout to all other tokens before
               projecting the
             representation to the original feature dimension D using a linear layer followed by a GELU non-linearity.
-        is_hybrid (`bool`, *optional*, defaults to `False`):
-            Whether to use a hybrid backbone. Useful in the context of loading DPT-Hybrid models.
         reassemble_factors (`List[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`):
             The up/downsampling factors of the reassemble layers.
-        neck_hidden_sizes (`List[str]`, *optional*, defaults to [96, 192, 384, 768]):
+        neck_hidden_sizes (`List[str]`, *optional*, defaults to `[96, 192, 384, 768]`):
             The hidden sizes to project to for the feature maps of the backbone.
         fusion_hidden_size (`int`, *optional*, defaults to 256):
             The number of channels before fusion.

src/transformers/models/dpt/image_processing_dpt.py

@@ -100,14 +100,14 @@ class DPTImageProcessor(BaseImageProcessor):
             Whether to resize the image's (height, width) dimensions. Can be overidden by `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`):
             Size of the image after resizing. Can be overidden by `size` in `preprocess`.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Defines the resampling filter to use if resizing the image. Can be overidden by `resample` in `preprocess`.
         keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
             If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
             be overidden by `keep_aspect_ratio` in `preprocess`.
         ensure_multiple_of (`int`, *optional*, defaults to 1):
             If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden
             by `ensure_multiple_of` in `preprocess`.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-            Defines the resampling filter to use if resizing the image. Can be overidden by `resample` in `preprocess`.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether to rescale the image by the specified scale `rescale_factor`. Can be overidden by `do_rescale` in
             `preprocess`.

src/transformers/models/efficientnet/image_processing_efficientnet.py

@@ -52,22 +52,22 @@ class EfficientNetImageProcessor(BaseImageProcessor):
             `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 346, "width": 346}`):
             Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.NEAREST`):
+        resample (`PILImageResampling` filter, *optional*, defaults to 0):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_center_crop (`bool`, *optional*, defaults to `False`):
             Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
             is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`.
         crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 289, "width": 289}`):
             Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
-            parameter in the `preprocess` method.
         rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
             Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
             `preprocess` method.
         rescale_offset (`bool`, *optional*, defaults to `False`):
             Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. Can be
             overridden by the `rescale_factor` parameter in the `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
             method.

src/transformers/models/falcon/configuration_falcon.py

@@ -46,13 +46,13 @@ class FalconConfig(PretrainedConfig):
             Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 71):
             Number of attention heads for each attention layer in the Transformer encoder.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether the model should return the last key/values attentions (not used by all models). Only relevant if
             `config.is_decoder=True`.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
         hidden_dropout (`float`, *optional*, defaults to 0.0):
             The dropout probability for MLP layers.
         attention_dropout (`float`, *optional*, defaults to 0.0):

src/transformers/models/flaubert/tokenization_flaubert.py

@@ -207,7 +207,7 @@ class FlaubertTokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<special1>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<special0>","<special1>","<special2>","<special3>","<special4>","<special5>","<special6>","<special7>","<special8>","<special9>"]`):
+        additional_special_tokens (`List[str]`, *optional*, defaults to `['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']`):
             List of additional special tokens.
         lang2id (`Dict[str, int]`, *optional*):
             Dictionary mapping languages string identifiers to their IDs.

src/transformers/models/flava/configuration_flava.py

@@ -52,9 +52,9 @@ class FlavaImageConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -291,7 +291,7 @@ class FlavaMultimodalConfig(PretrainedConfig):
     Args:
         hidden_size (`int`, *optional*, defaults to 768):
             Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
+        num_hidden_layers (`int`, *optional*, defaults to 6):
             Number of hidden layers in the Transformer encoder.
         num_attention_heads (`int`, *optional*, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
@@ -300,9 +300,9 @@ class FlavaMultimodalConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

src/transformers/models/flava/processing_flava.py

@@ -33,8 +33,8 @@ class FlavaProcessor(ProcessorMixin):
     [`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`FlavaImageProcessor`]): The image processor is a required input.
+        image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input.
-        tokenizer ([`BertTokenizerFast`]): The tokenizer is a required input.
+        tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "FlavaImageProcessor"

src/transformers/models/focalnet/configuration_focalnet.py

@@ -67,7 +67,7 @@ class FocalNetConfig(BackboneConfigMixin, PretrainedConfig):
             Stochastic depth rate.
         use_layerscale (`bool`, *optional*, defaults to `False`):
             Whether to use layer scale in the encoder.
-        layerscale_value (`float`, *optional*, defaults to 1e-4):
+        layerscale_value (`float`, *optional*, defaults to 0.0001):
             The initial value of the layer scale.
         use_post_layernorm (`bool`, *optional*, defaults to `False`):
             Whether to use post layer normalization in the encoder.
@@ -77,9 +77,9 @@ class FocalNetConfig(BackboneConfigMixin, PretrainedConfig):
             Whether to normalize the modulator.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
-        encoder_stride (`int`, `optional`, defaults to 32):
+        encoder_stride (`int`, *optional*, defaults to 32):
             Factor to increase the spatial resolution by in the decoder head for masked image modeling.
         out_features (`List[str]`, *optional*):
             If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.

src/transformers/models/fsmt/tokenization_fsmt.py

@@ -146,13 +146,13 @@ class FSMTTokenizer(PreTrainedTokenizer):
     this superclass for more information regarding those methods.
 
     Args:
-        langs (`List[str]`):
+        langs (`List[str]`, *optional*):
             A list of two languages to translate from and to, for instance `["en", "ru"]`.
-        src_vocab_file (`str`):
+        src_vocab_file (`str`, *optional*):
             File containing the vocabulary for the source language.
-        tgt_vocab_file (`st`):
+        tgt_vocab_file (`st`, *optional*):
             File containing the vocabulary for the target language.
-        merges_file (`str`):
+        merges_file (`str`, *optional*):
             File containing the merges.
         do_lower_case (`bool`, *optional*, defaults to `False`):
             Whether or not to lowercase the input when tokenizing.

src/transformers/models/funnel/configuration_funnel.py

@@ -81,7 +81,7 @@ class FunnelConfig(PretrainedConfig):
             The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of
             linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for
             linear layers.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-9):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-09):
             The epsilon used by the layer normalization layers.
         pooling_type (`str`, *optional*, defaults to `"mean"`):
             Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block.
@@ -90,10 +90,10 @@ class FunnelConfig(PretrainedConfig):
             is faster on TPU.
         separate_cls (`bool`, *optional*, defaults to `True`):
             Whether or not to separate the cls token when applying pooling.
-        truncate_seq (`bool`, *optional*, defaults to `False`):
+        truncate_seq (`bool`, *optional*, defaults to `True`):
             When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a
             sequence length that is not a multiple of 2.
-        pool_q_only (`bool`, *optional*, defaults to `False`):
+        pool_q_only (`bool`, *optional*, defaults to `True`):
             Whether or not to apply the pooling only to the query or to query, key and values for the attention layers.
     """
     model_type = "funnel"

src/transformers/models/funnel/tokenization_funnel.py

@@ -120,9 +120,9 @@ class FunnelTokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<mask>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        bos_token (`str`, `optional`, defaults to `"<s>"`):
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
             The beginning of sentence token.
-        eos_token (`str`, `optional`, defaults to `"</s>"`):
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
             The end of sentence token.
         tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
             Whether or not to tokenize Chinese characters.

src/transformers/models/glpn/configuration_glpn.py

@@ -51,7 +51,7 @@ class GLPNConfig(PretrainedConfig):
             Patch size before each encoder block.
         strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
             Stride before each encoder block.
-        num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 4, 8]`):
+        num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
             Number of attention heads for each attention layer in each block of the Transformer encoder.
         mlp_ratios (`List[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
             Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
@@ -67,9 +67,9 @@ class GLPNConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         drop_path_rate (`float`, *optional*, defaults to 0.1):
             The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the layer normalization layers.
-        decoder_hidden_size (`int`, *optional*, defaults to 32):
+        decoder_hidden_size (`int`, *optional*, defaults to 64):
             The dimension of the decoder.
         max_depth (`int`, *optional*, defaults to 10):
             The maximum depth of the decoder.

src/transformers/models/glpn/image_processing_glpn.py

@@ -48,7 +48,7 @@ class GLPNImageProcessor(BaseImageProcessor):
         size_divisor (`int`, *optional*, defaults to 32):
             When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest
             multiple of `size_divisor`. Can be overridden by `size_divisor` in `preprocess`.
-        resample (`PIL.Image` resampling filter, *optional*, defaults to `PILImageResampling.BILINEAR`):
+        resample (`PIL.Image` resampling filter, *optional*, defaults to `Resampling.BILINEAR`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be

src/transformers/models/gpt_neo/configuration_gpt_neo.py

@@ -54,7 +54,7 @@ class GPTNeoConfig(PretrainedConfig):
             Dimensionality of the encoder layers and the pooler layer.
         num_layers (`int`, *optional*, defaults to 24):
             Number of hidden layers in the Transformer encoder.
-        attention_types (`List`, *optional*, defaults to `[[["global", "local"], 12]]`):
+        attention_types (`List`, *optional*, defaults to `[[['global', 'local'], 12]]`):
             The type of attention for each layer in a `List` of the following format `[[["attention_type"],
             num_layerss]]` e.g. for a 24 layer model `[[["global"], 24]]` or `[[["global", "local"], 12]]` Choose the
             value of `attention_type` from `["global", "local"]`
@@ -76,7 +76,7 @@ class GPTNeoConfig(PretrainedConfig):
         classifier_dropout (`float`, *optional*, defaults to 0.1):
             Argument used when doing token classification, used in the model [`GPTNeoForTokenClassification`]. The
             dropout ratio for the hidden layer.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py

@@ -64,17 +64,17 @@ class GPTSw3Tokenizer(PreTrainedTokenizer):
             Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
         keep_accents (`bool`, *optional*, defaults to `False`):
             Whether or not to keep accents when tokenizing.
-        bos_token (`str`, *optional*):
-            The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
-            not provided, will default to '<s>' or '<|endoftext|>', depending on model size.
-        eos_token (`str`, *optional*):
-            The end of sequence token seen during pretraining. If not provided, will default to '<|endoftext|>'
-        unk_token (`str`, *optional*):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead. If not provided, will default to '<unk>'.
         pad_token (`str`, *optional*):
             The token used for padding, for example when batching sequences of different lengths. If not provided, will
             default to '<pad>' or '<unk>' depending on model size.
+        unk_token (`str`, *optional*):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead. If not provided, will default to '<unk>'.
+        eos_token (`str`, *optional*):
+            The end of sequence token seen during pretraining. If not provided, will default to '<|endoftext|>'
+        bos_token (`str`, *optional*):
+            The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
+            not provided, will default to '<s>' or '<|endoftext|>', depending on model size.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
             SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,

src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py

@@ -139,7 +139,7 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
             The token used for unknown charactor
         pad_token (`str`, *optional*, defaults to `"<|separator|>"`):
             The token used for padding
-        bos_token (`str`, *optional*, defaults to `"<|startoftext|>""`):
+        bos_token (`str`, *optional*, defaults to `"<|startoftext|>"`):
             The beginning of sequence token.
         eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
             The end of sequence token.

src/transformers/models/idefics/image_processing_idefics.py

@@ -53,10 +53,8 @@ class IdeficsImageProcessor(BaseImageProcessor):
     Constructs a Idefics image processor.
 
     Args:
-        image_size (`int`, *optional*, defaults to `224`):
+        image_size (`int`, *optional*, defaults to 224):
             Resize to image size
-        image_num_channels (`int`, *optional*, defaults to `3`):
-            Number of image channels.
         image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
             Mean to use if normalizing the image. This is a float or list of floats the length of the number of
             channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
@@ -65,6 +63,8 @@ class IdeficsImageProcessor(BaseImageProcessor):
             Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
             number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
             Can be overridden by the `image_std` parameter in the `preprocess` method.
+        image_num_channels (`int`, *optional*, defaults to 3):
+            Number of image channels.
     """
 
     model_input_names = ["pixel_values"]

src/transformers/models/imagegpt/image_processing_imagegpt.py

@@ -70,7 +70,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
             `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
             Size of the image after resizing. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in

src/transformers/models/instructblip/configuration_instructblip.py

@@ -57,7 +57,7 @@ class InstructBlipVisionConfig(PretrainedConfig):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. to 1e-5): The epsilon used by the layer
             normalization layers.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the layer normalization layers.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.

src/transformers/models/layoutlm/configuration_layoutlm.py

@@ -83,8 +83,6 @@ class LayoutLMConfig(PretrainedConfig):
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the model should return the last key/values attentions (not used by all models). Only
             relevant if `config.is_decoder=True`.
-        classifier_dropout (`float`, *optional*):
-            The dropout ratio for the classification head.
         max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
             The maximum value that the 2D position embedding might ever used. Typically set this to something large
             just in case (e.g., 1024).

src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py

@@ -100,7 +100,7 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
             overridden by `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
             Size of the image after resizing. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
             Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
             `preprocess` method.
         apply_ocr (`bool`, *optional*, defaults to `True`):
@@ -109,7 +109,7 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
         ocr_lang (`str`, *optional*):
             The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
             used. Can be overridden by `ocr_lang` in `preprocess`.
-        tesseract_config (`str`, *optional*):
+        tesseract_config (`str`, *optional*, defaults to `""`):
             Any additional custom configuration flags that are forwarded to the `config` parameter when calling
             Tesseract. For example: '--psm 6'. Can be overridden by `tesseract_config` in `preprocess`.
     """

src/transformers/models/layoutlmv2/processing_layoutlmv2.py

@@ -38,9 +38,9 @@ class LayoutLMv2Processor(ProcessorMixin):
     into token-level `labels` for token classification tasks (such as FUNSD, CORD).
 
     Args:
-        image_processor (`LayoutLMv2ImageProcessor`):
+        image_processor (`LayoutLMv2ImageProcessor`, *optional*):
             An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
-        tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`):
+        tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`, *optional*):
             An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/layoutlmv3/processing_layoutlmv3.py

@@ -38,9 +38,9 @@ class LayoutLMv3Processor(ProcessorMixin):
     into token-level `labels` for token classification tasks (such as FUNSD, CORD).
 
     Args:
-        image_processor (`LayoutLMv3ImageProcessor`):
+        image_processor (`LayoutLMv3ImageProcessor`, *optional*):
             An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
-        tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`):
+        tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`, *optional*):
             An instance of [`LayoutLMv3Tokenizer`] or [`LayoutLMv3TokenizerFast`]. The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py

@@ -253,7 +253,7 @@ class LayoutLMv3Tokenizer(PreTrainedTokenizer):
         mask_token (`str`, *optional*, defaults to `"<mask>"`):
             The token used for masking values. This is the token used when training this model with masked language
             modeling. This is the token which the model will try to predict.
-        add_prefix_space (`bool`, *optional*, defaults to `False`):
+        add_prefix_space (`bool`, *optional*, defaults to `True`):
             Whether or not to add an initial space to the input. This allows to treat the leading word just as any
             other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
         cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):

src/transformers/models/layoutxlm/processing_layoutxlm.py

@@ -37,9 +37,9 @@ class LayoutXLMProcessor(ProcessorMixin):
     into token-level `labels` for token classification tasks (such as FUNSD, CORD).
 
     Args:
-        image_processor (`LayoutLMv2ImageProcessor`):
+        image_processor (`LayoutLMv2ImageProcessor`, *optional*):
             An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
-        tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`):
+        tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`, *optional*):
             An instance of [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`]. The tokenizer is a required input.
     """
 

src/transformers/models/layoutxlm/tokenization_layoutxlm.py

@@ -203,8 +203,6 @@ class LayoutXLMTokenizer(PreTrainedTokenizer):
             CrossEntropyLoss.
         only_label_first_subword (`bool`, *optional*, defaults to `True`):
             Whether or not to only label the first subword, in case word labels are provided.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
             SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,

src/transformers/models/levit/image_processing_levit.py

@@ -56,7 +56,7 @@ class LevitImageProcessor(BaseImageProcessor):
             edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this
             value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width,
             size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
             `preprocess` method.
         do_center_crop (`bool`, *optional*, defaults to `True`):
@@ -74,10 +74,10 @@ class LevitImageProcessor(BaseImageProcessor):
         do_normalize (`bool`, *optional*, defaults to `True`):
             Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
             `preprocess` method.
-        image_mean (`List[int]`, defaults to `[0.229, 0.224, 0.225]`):
+        image_mean (`List[int]`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
             Mean to use if normalizing the image. This is a float or list of floats the length of the number of
             channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`List[int]`, defaults to `[0.485, 0.456, 0.406]`):
+        image_std (`List[int]`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
             Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
             number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
     """

src/transformers/models/lxmert/configuration_lxmert.py

@@ -43,14 +43,18 @@ class LxmertConfig(PretrainedConfig):
             `inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
         hidden_size (`int`, *optional*, defaults to 768):
             Dimensionality of the encoder layers and the pooler layer.
-        r_layers (`int`, *optional*, defaults to 5):
+        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer visual encoder.
-        l_layers (`int`, *optional*, defaults to 9):
-            Number of hidden layers in the Transformer language encoder.
-        x_layers (`int`, *optional*, defaults to 5):
-            Number of hidden layers in the Transformer cross modality encoder.
-        num_attention_heads (`int`, *optional*, defaults to 5):
             Number of attention heads for each attention layer in the Transformer encoder.
+        num_qa_labels (`int`, *optional*, defaults to 9500):
+            This represents the total number of different question answering (QA) labels there are. If using more than
+            one dataset with QA, the user will need to account for the total number of labels that all of the datasets
+            have in total.
+        num_object_labels (`int`, *optional*, defaults to 1600):
+            This represents the total number of semantically unique objects that lxmert will be able to classify a
+            pooled-object feature as belonging too.
+        num_attr_labels (`int`, *optional*, defaults to 400):
+            This represents the total number of semantically unique attributes that lxmert will be able to classify a
+            pooled-object feature as possessing.
         intermediate_size (`int`, *optional*, defaults to 3072):
             Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
         hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
@@ -69,25 +73,21 @@ class LxmertConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
+        l_layers (`int`, *optional*, defaults to 9):
+            Number of hidden layers in the Transformer language encoder.
+        x_layers (`int`, *optional*, defaults to 5):
+            Number of hidden layers in the Transformer cross modality encoder.
+        r_layers (`int`, *optional*, defaults to 5):
+            Number of hidden layers in the Transformer visual encoder.
         visual_feat_dim (`int`, *optional*, defaults to 2048):
             This represents the last dimension of the pooled-object features used as input for the model, representing
             the size of each object feature itself.
         visual_pos_dim (`int`, *optional*, defaults to 4):
             This represents the number of spacial features that are mixed into the visual features. The default is set
             to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
-        visual_loss_normalizer (`float`, *optional*, defaults to 1/15):
+        visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
             This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
             decided to train with multiple vision-based loss objectives.
-        num_qa_labels (`int`, *optional*, defaults to 9500):
-            This represents the total number of different question answering (QA) labels there are. If using more than
-            one dataset with QA, the user will need to account for the total number of labels that all of the datasets
-            have in total.
-        num_object_labels (`int`, *optional*, defaults to 1600):
-            This represents the total number of semantically unique objects that lxmert will be able to classify a
-            pooled-object feature as belonging too.
-        num_attr_labels (`int`, *optional*, defaults to 400):
-            This represents the total number of semantically unique attributes that lxmert will be able to classify a
-            pooled-object feature as possessing.
         task_matched (`bool`, *optional*, defaults to `True`):
             This task is used for sentence-image matching. If the sentence correctly describes the image the label will
             be 1. If the sentence does not correctly describe the image, the label will be 0.
@@ -104,12 +104,6 @@ class LxmertConfig(PretrainedConfig):
             Whether or not to calculate the attribute-prediction loss objective
         visual_feat_loss (`bool`, *optional*, defaults to `True`):
             Whether or not to calculate the feature-regression loss objective
-        output_attentions (`bool`, *optional*, defaults to `False`):
-            Whether or not the model should return the attentions from the vision, language, and cross-modality layers
-            should be returned.
-        output_hidden_states (`bool`, *optional*, defaults to `False`):
-            Whether or not the model should return the hidden states from the vision, language, and cross-modality
-            layers should be returned.
     """
 
     model_type = "lxmert"

src/transformers/models/mask2former/image_processing_mask2former.py

@@ -356,20 +356,17 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
             sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
             the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
             height / width, size)`.
-        max_size (`int`, *optional*, defaults to 1333):
+        size_divisor (`int`, *optional*, defaults to 32):
-            The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
+            Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
-            set to `True`.
+            Swin Transformer.
-        resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
+        resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
             An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
             `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
             `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
             to `True`.
-        size_divisor (`int`, *optional*, defaults to 32):
-            Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
-            Swin Transformer.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether to rescale the input to a certain `scale`.
-        rescale_factor (`float`, *optional*, defaults to 1/ 255):
+        rescale_factor (`float`, *optional*, defaults to `1/ 255`):
             Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether or not to normalize the input with mean and standard deviation.

src/transformers/models/maskformer/image_processing_maskformer.py

@@ -358,20 +358,17 @@ class MaskFormerImageProcessor(BaseImageProcessor):
             sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
             the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
             height / width, size)`.
-        max_size (`int`, *optional*, defaults to 1333):
+        size_divisor (`int`, *optional*, defaults to 32):
-            The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
+            Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
-            set to `True`.
+            Swin Transformer.
-        resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
+        resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
             An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
             `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
             `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
             to `True`.
-        size_divisor (`int`, *optional*, defaults to 32):
-            Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
-            Swin Transformer.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether to rescale the input to a certain `scale`.
-        rescale_factor (`float`, *optional*, defaults to 1/ 255):
+        rescale_factor (`float`, *optional*, defaults to `1/ 255`):
             Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether or not to normalize the input with mean and standard deviation.

src/transformers/models/mgp_str/configuration_mgp_str.py

@@ -62,7 +62,7 @@ class MgpstrConfig(PretrainedConfig):
             Whether to add a bias to the queries, keys and values.
         distilled (`bool`, *optional*, defaults to `False`):
             Model includes a distillation token and head as in DeiT models.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         drop_rate (`float`, *optional*, defaults to 0.0):
             The dropout probability for all fully connected layers in the embeddings, encoder.

src/transformers/models/mgp_str/processing_mgp_str.py

@@ -44,9 +44,9 @@ class MgpstrProcessor(ProcessorMixin):
     [`~MgpstrProcessor.__call__`] and [`~MgpstrProcessor.batch_decode`] for more information.
 
     Args:
-        image_processor (`ViTImageProcessor`):
+        image_processor (`ViTImageProcessor`, *optional*):
             An instance of `ViTImageProcessor`. The image processor is a required input.
-        tokenizer ([`MgpstrTokenizer`]):
+        tokenizer ([`MgpstrTokenizer`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "char_tokenizer"]

src/transformers/models/mgp_str/tokenization_mgp_str.py

@@ -52,7 +52,7 @@ class MgpstrTokenizer(PreTrainedTokenizer):
             The beginning of sequence token.
         eos_token (`str`, *optional*, defaults to `"[s]"`):
             The end of sequence token.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*, , defaults to `"[GO]"`):
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"[GO]"`):
             A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
             attention mechanisms or loss computation.
     """

src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py

@@ -55,7 +55,7 @@ class MobileNetV1Config(PretrainedConfig):
             All layers will have at least this many channels.
         hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
             The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
-        tf_padding (`bool`, `optional`, defaults to `True`):
+        tf_padding (`bool`, *optional*, defaults to `True`):
             Whether to use TensorFlow padding rules on the convolution layers.
         classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
             The dropout ratio for attached classifiers.

src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py

@@ -64,16 +64,16 @@ class MobileNetV2Config(PretrainedConfig):
             the input dimensions by a factor of 32. If `output_stride` is 8 or 16, the model uses dilated convolutions
             on the depthwise layers instead of regular convolutions, so that the feature maps never become more than 8x
             or 16x smaller than the input image.
-        first_layer_is_expansion (`bool`, `optional`, defaults to `True`):
+        first_layer_is_expansion (`bool`, *optional*, defaults to `True`):
             True if the very first convolution layer is also the expansion layer for the first expansion block.
-        finegrained_output (`bool`, `optional`, defaults to `True`):
+        finegrained_output (`bool`, *optional*, defaults to `True`):
             If true, the number of output channels in the final convolution layer will stay large (1280) even if
             `depth_multiplier` is less than 1.
         hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
             The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
-        tf_padding (`bool`, `optional`, defaults to `True`):
+        tf_padding (`bool`, *optional*, defaults to `True`):
             Whether to use TensorFlow padding rules on the convolution layers.
-        classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
+        classifier_dropout_prob (`float`, *optional*, defaults to 0.8):
             The dropout ratio for attached classifiers.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -105,7 +105,7 @@ class MobileNetV2Config(PretrainedConfig):
         depth_multiplier=1.0,
         depth_divisible_by=8,
         min_depth=8,
-        expand_ratio=6,
+        expand_ratio=6.0,
         output_stride=32,
         first_layer_is_expansion=True,
         finegrained_output=True,

src/transformers/models/mobilevit/configuration_mobilevit.py

@@ -74,7 +74,7 @@ class MobileViTConfig(PretrainedConfig):
             The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
         conv_kernel_size (`int`, *optional*, defaults to 3):
             The size of the convolutional kernel in the MobileViT layer.
-        output_stride (`int`, `optional`, defaults to 32):
+        output_stride (`int`, *optional*, defaults to 32):
             The ratio of the spatial resolution of the output to the resolution of the input image.
         hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the Transformer encoder.
@@ -84,11 +84,11 @@ class MobileViTConfig(PretrainedConfig):
             The dropout ratio for attached classifiers.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether to add a bias to the queries, keys and values.
-        aspp_out_channels (`int`, `optional`, defaults to 256):
+        aspp_out_channels (`int`, *optional*, defaults to 256):
             Number of output channels used in the ASPP layer for semantic segmentation.
         atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`):
             Dilation (atrous) factors used in the ASPP layer for semantic segmentation.

src/transformers/models/mobilevit/image_processing_mobilevit.py

@@ -59,7 +59,7 @@ class MobileViTImageProcessor(BaseImageProcessor):
         size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
             Controls the size of the output image after resizing. Can be overridden by the `size` parameter in the
             `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
             Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter
             in the `preprocess` method.
         do_rescale (`bool`, *optional*, defaults to `True`):

src/transformers/models/mobilevitv2/configuration_mobilevitv2.py

@@ -54,15 +54,15 @@ class MobileViTV2Config(PretrainedConfig):
             The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
         conv_kernel_size (`int`, *optional*, defaults to 3):
             The size of the convolutional kernel in the MobileViTV2 layer.
-        output_stride (`int`, `optional`, defaults to 32):
+        output_stride (`int`, *optional*, defaults to 32):
             The ratio of the spatial resolution of the output to the resolution of the input image.
         classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
             The dropout ratio for attached classifiers.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
-        aspp_out_channels (`int`, `optional`, defaults to 512):
+        aspp_out_channels (`int`, *optional*, defaults to 512):
             Number of output channels used in the ASPP layer for semantic segmentation.
         atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`):
             Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
@@ -74,13 +74,13 @@ class MobileViTV2Config(PretrainedConfig):
             The number of attention blocks in each MobileViTV2Layer
         base_attn_unit_dims (`List[int]`, *optional*, defaults to `[128, 192, 256]`):
             The base multiplier for dimensions of attention blocks in each MobileViTV2Layer
-        width_multiplier (`float`, *optional*, defaults to 1.0)
+        width_multiplier (`float`, *optional*, defaults to 1.0):
             The width multiplier for MobileViTV2.
-        ffn_multiplier (`int`, *optional*, defaults to 2)
+        ffn_multiplier (`int`, *optional*, defaults to 2):
             The FFN multiplier for MobileViTV2.
-        attn_dropout (`float`, *optional*, defaults to 0.0)
+        attn_dropout (`float`, *optional*, defaults to 0.0):
             The dropout in the attention layer.
-        ffn_dropout (`float`, *optional*, defaults to 0.0)
+        ffn_dropout (`float`, *optional*, defaults to 0.0):
             The dropout between FFN layers.
 
     Example:

src/transformers/models/mpt/configuration_mpt.py

@@ -145,17 +145,17 @@ class MptConfig(PretrainedConfig):
             the `inputs_ids` passed when calling [`MptModel`]. Check [this
             discussion](https://huggingface.co/bigscience/mpt/discussions/120#633d28389addb8530b406c2a) on how the
             `vocab_size` has been defined.
-        resid_pdrop (`float`, *optional*, defaults to 0.1):
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
             The dropout probability applied to the attention output before combining with residual.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon to use in the layer normalization layers.
-        emb_pdrop (`float`, *optional*, defaults to 0.1):
+        emb_pdrop (`float`, *optional*, defaults to 0.0):
             The dropout probability for the embedding layer.
-        learned_pos_emb (`bool`, *optional*, defaults to `False`):
+        learned_pos_emb (`bool`, *optional*, defaults to `True`):
             Whether to use learned positional embeddings.
         attn_config (`dict`, *optional*):
             A dictionary used to configure the model's attention module.
-        init_device (`str`, *optional*):
+        init_device (`str`, *optional*, defaults to `"cpu"`):
             The device to use for parameter initialization. Defined for backward compatibility
         logit_scale (`float`, *optional*):
             If not None, scale the logits by this value.
@@ -169,7 +169,7 @@ class MptConfig(PretrainedConfig):
         norm_type (`str`, *optional*, defaults to `"low_precision_layernorm"`):
             Type of layer norm to use. All MPT models uses the same layer norm implementation. Defined for backward
             compatibility.
-        use_cache (`bool`, *optional*, defaults to `True`):
+        use_cache (`bool`, *optional*, defaults to `False`):
             Whether or not the model should return the last key/values attentions (not used by all models).
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

src/transformers/models/nat/configuration_nat.py

@@ -44,9 +44,9 @@ class NatConfig(BackboneConfigMixin, PretrainedConfig):
             The number of input channels.
         embed_dim (`int`, *optional*, defaults to 64):
             Dimensionality of patch embedding.
-        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
+        depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
             Number of layers in each level of the encoder.
-        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
+        num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
             Number of attention heads in each layer of the Transformer encoder.
         kernel_size (`int`, *optional*, defaults to 7):
             Neighborhood Attention kernel size.
@@ -65,7 +65,7 @@ class NatConfig(BackboneConfigMixin, PretrainedConfig):
             `"selu"` and `"gelu_new"` are supported.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         layer_scale_init_value (`float`, *optional*, defaults to 0.0):
             The initial value for the layer scale. Disabled if <=0.

src/transformers/models/nougat/image_processing_nougat.py

@@ -66,7 +66,7 @@ class NougatImageProcessor(BaseImageProcessor):
             `do_resize` in the `preprocess` method.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 896, "width": 672}`):
             Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
         do_thumbnail (`bool`, *optional*, defaults to `True`):
             Whether to resize the image using thumbnail method.

src/transformers/models/nougat/tokenization_nougat_fast.py

@@ -383,10 +383,10 @@ class NougatTokenizerFast(PreTrainedTokenizerFast):
     methods for postprocessing the generated text.
 
     Args:
-        vocab_file (`str`):
+        vocab_file (`str`, *optional*):
             [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
             contains the vocabulary necessary to instantiate a tokenizer.
-        tokenizer_file (`str`):
+        tokenizer_file (`str`, *optional*):
             [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
             contains everything needed to load the tokenizer.
 
@@ -394,16 +394,16 @@ class NougatTokenizerFast(PreTrainedTokenizerFast):
             Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
             spaces.
 
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+
         bos_token (`str`, *optional*, defaults to `"<s>"`):
             The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
 
         eos_token (`str`, *optional*, defaults to `"</s>"`):
             The end of sequence token.
 
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-
         pad_token (`str`, *optional*, defaults to `"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
     """

src/transformers/models/oneformer/configuration_oneformer.py

@@ -42,87 +42,87 @@ class OneFormerConfig(PretrainedConfig):
     documentation from [`PretrainedConfig`] for more information.
 
     Args:
-        backbone_config (`PretrainedConfig`, *optional*, defaults to `SwinConfig`)
+        backbone_config (`PretrainedConfig`, *optional*, defaults to `SwinConfig`):
             The configuration of the backbone model.
-        ignore_value (`int`, *optional*, defaults to 255)
+        ignore_value (`int`, *optional*, defaults to 255):
             Values to be ignored in GT label while calculating loss.
-        num_queries (`int`, *optional*, defaults to 150)
+        num_queries (`int`, *optional*, defaults to 150):
             Number of object queries.
-        no_object_weight (`float`, *optional*, defaults to 0.1)
+        no_object_weight (`float`, *optional*, defaults to 0.1):
             Weight for no-object class predictions.
-        class_weight (`float`, *optional*, defaults to 2.0)
+        class_weight (`float`, *optional*, defaults to 2.0):
             Weight for Classification CE loss.
-        mask_weight (`float`, *optional*, defaults to 5.0)
+        mask_weight (`float`, *optional*, defaults to 5.0):
             Weight for binary CE loss.
-        dice_weight (`float`, *optional*, defaults to 5.0)
+        dice_weight (`float`, *optional*, defaults to 5.0):
             Weight for dice loss.
-        contrastive_weight (`float`, *optional*, defaults to 0.5)
+        contrastive_weight (`float`, *optional*, defaults to 0.5):
             Weight for contrastive loss.
-        contrastive_temperature (`float`, *optional*, defaults to 0.07)
+        contrastive_temperature (`float`, *optional*, defaults to 0.07):
             Initial value for scaling the contrastive logits.
-        train_num_points (`int`, *optional*, defaults to 12544)
+        train_num_points (`int`, *optional*, defaults to 12544):
             Number of points to sample while calculating losses on mask predictions.
-        oversample_ratio (`float`, *optional*, defaults to 3.0)
+        oversample_ratio (`float`, *optional*, defaults to 3.0):
             Ratio to decide how many points to oversample.
-        importance_sample_ratio (`float`, *optional*, defaults to 0.75)
+        importance_sample_ratio (`float`, *optional*, defaults to 0.75):
             Ratio of points that are sampled via importance sampling.
-        init_std (`float`, *optional*, defaults to 0.02)
+        init_std (`float`, *optional*, defaults to 0.02):
             Standard deviation for normal intialization.
-        init_xavier_std (`float`, *optional*, defaults to 0.02)
+        init_xavier_std (`float`, *optional*, defaults to 1.0):
             Standard deviation for xavier uniform initialization.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05)
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             Epsilon for layer normalization.
-        is_training (`bool`, *optional*, defaults to False)
+        is_training (`bool`, *optional*, defaults to `False`):
             Whether to run in training or inference mode.
-        use_auxiliary_loss (`bool`, *optional*, defaults to True)
+        use_auxiliary_loss (`bool`, *optional*, defaults to `True`):
             Whether to calculate loss using intermediate predictions from transformer decoder.
-        output_auxiliary_logits (`bool`, *optional*, defaults to True)
+        output_auxiliary_logits (`bool`, *optional*, defaults to `True`):
             Whether to return intermediate predictions from transformer decoder.
-        strides (`list`, *optional*, defaults to [4, 8, 16, 32])
+        strides (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
             List containing the strides for feature maps in the encoder.
-        task_seq_len (`int`, *optional*, defaults to 77)
+        task_seq_len (`int`, *optional*, defaults to 77):
             Sequence length for tokenizing text list input.
-        text_encoder_width (`int`, *optional*, defaults to 256)
+        text_encoder_width (`int`, *optional*, defaults to 256):
             Hidden size for text encoder.
         text_encoder_context_length (`int`, *optional*, defaults to 77):
             Input sequence length for text encoder.
-        text_encoder_num_layers (`int`, *optional*, defaults to 6)
+        text_encoder_num_layers (`int`, *optional*, defaults to 6):
             Number of layers for transformer in text encoder.
-        text_encoder_vocab_size (`int`, *optional*, defaults to 49408)
+        text_encoder_vocab_size (`int`, *optional*, defaults to 49408):
             Vocabulary size for tokenizer.
-        text_encoder_proj_layers (`int`, *optional*, defaults to 2)
+        text_encoder_proj_layers (`int`, *optional*, defaults to 2):
             Number of layers in MLP for project text queries.
-        text_encoder_n_ctx (`int`, *optional*, defaults to 16)
+        text_encoder_n_ctx (`int`, *optional*, defaults to 16):
             Number of learnable text context queries.
-        conv_dim (`int`, *optional*, defaults to 256)
+        conv_dim (`int`, *optional*, defaults to 256):
             Feature map dimension to map outputs from the backbone.
-        mask_dim (`int`, *optional*, defaults to 256)
+        mask_dim (`int`, *optional*, defaults to 256):
             Dimension for feature maps in pixel decoder.
-        hidden_dim (`int`, *optional*, defaults to 256)
+        hidden_dim (`int`, *optional*, defaults to 256):
             Dimension for hidden states in transformer decoder.
-        encoder_feedforward_dim (`int`, *optional*, defaults to 1024)
+        encoder_feedforward_dim (`int`, *optional*, defaults to 1024):
             Dimension for FFN layer in pixel decoder.
-        norm (`str`, *optional*, defaults to `GN`)
+        norm (`str`, *optional*, defaults to `"GN"`):
             Type of normalization.
-        encoder_layers (`int`, *optional*, defaults to 6)
+        encoder_layers (`int`, *optional*, defaults to 6):
             Number of layers in pixel decoder.
-        decoder_layers (`int`, *optional*, defaults to 10)
+        decoder_layers (`int`, *optional*, defaults to 10):
             Number of layers in transformer decoder.
-        use_task_norm (`bool`, *optional*, defaults to `True`)
+        use_task_norm (`bool`, *optional*, defaults to `True`):
             Whether to normalize the task token.
-        num_attention_heads (`int`, *optional*, defaults to 8)
+        num_attention_heads (`int`, *optional*, defaults to 8):
             Number of attention heads in transformer layers in the pixel and transformer decoders.
-        dropout (`float`, *optional*, defaults to 0.1)
+        dropout (`float`, *optional*, defaults to 0.1):
             Dropout probability for pixel and transformer decoders.
-        dim_feedforward (`int`, *optional*, defaults to 2048)
+        dim_feedforward (`int`, *optional*, defaults to 2048):
             Dimension for FFN layer in transformer decoder.
-        pre_norm (`bool`, *optional*, defaults to `False`)
+        pre_norm (`bool`, *optional*, defaults to `False`):
             Whether to normalize hidden states before attention layers in transformer decoder.
-        enforce_input_proj (`bool`, *optional*, defaults to `False`)
+        enforce_input_proj (`bool`, *optional*, defaults to `False`):
             Whether to project hidden states in transformer decoder.
-        query_dec_layers (`int`, *optional*, defaults to 2)
+        query_dec_layers (`int`, *optional*, defaults to 2):
             Number of layers in query transformer.
-        common_stride (`int`, *optional*, defaults to 4)
+        common_stride (`int`, *optional*, defaults to 4):
             Common stride used for features in pixel decoder.
 
     Examples:

src/transformers/models/oneformer/image_processing_oneformer.py

@@ -361,17 +361,14 @@ class OneFormerImageProcessor(BaseImageProcessor):
             sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
             the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
             height / width, size)`.
-        max_size (`int`, *optional*, defaults to 1333):
+        resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
-            The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
-            set to `True`.
-        resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
             An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
             `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
             `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
             to `True`.
         do_rescale (`bool`, *optional*, defaults to `True`):
             Whether to rescale the input to a certain `scale`.
-        rescale_factor (`float`, *optional*, defaults to 1/ 255):
+        rescale_factor (`float`, *optional*, defaults to `1/ 255`):
             Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
         do_normalize (`bool`, *optional*, defaults to `True`):
             Whether or not to normalize the input with mean and standard deviation.
@@ -387,9 +384,9 @@ class OneFormerImageProcessor(BaseImageProcessor):
             Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
             is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
             The background label will be replaced by `ignore_index`.
-        repo_path (`str`, defaults to `shi-labs/oneformer_demo`):
+        repo_path (`str`, defaults to `shi-labs/oneformer_demo`, *optional*, defaults to `"shi-labs/oneformer_demo"`):
             Dataset repository on huggingface hub containing the JSON file with class information for the dataset.
-        class_info_file (`str`):
+        class_info_file (`str`, *optional*):
             JSON file containing class information for the dataset. It is stored inside on the `repo_path` dataset
             repository.
         num_text (`int`, *optional*):

src/transformers/models/openai/configuration_openai.py

@@ -56,7 +56,7 @@ class OpenAIGPTConfig(PretrainedConfig):
             The dropout ratio for the embeddings.
         attn_pdrop (`float`, *optional*, defaults to 0.1):
             The dropout ratio for the attention.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon to use in the layer normalization layers
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -91,8 +91,6 @@ class OpenAIGPTConfig(PretrainedConfig):
             [`OpenAIGPTDoubleHeadsModel`].
 
             The dropout ratio to be used after the projection and activation.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
 
 
     Examples:

src/transformers/models/owlvit/configuration_owlvit.py

@@ -171,13 +171,13 @@ class OwlViTVisionConfig(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
             `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        initializer_factor (`float``, *optional*, defaults to 1):
+        initializer_factor (`float``, *optional*, defaults to 1.0):
             A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
             testing).
 

src/transformers/models/owlvit/image_processing_owlvit.py

@@ -102,7 +102,7 @@ class OwlViTImageProcessor(BaseImageProcessor):
             The size to use for resizing the image. Only has an effect if `do_resize` is set to `True`. If `size` is a
             sequence like (h, w), output size will be matched to this. If `size` is an int, then image will be resized
             to (size, size).
-        resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`):
+        resample (`int`, *optional*, defaults to `Resampling.BICUBIC`):
             An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
             `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
             `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set

src/transformers/models/owlvit/processing_owlvit.py

@@ -33,9 +33,9 @@ class OwlViTProcessor(ProcessorMixin):
     [`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`OwlViTImageProcessor`]):
+        image_processor ([`OwlViTImageProcessor`], *optional*):
             The image processor is a required input.
-        tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
+        tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`], *optional*):
             The tokenizer is a required input.
     """
     attributes = ["image_processor", "tokenizer"]

src/transformers/models/perceiver/configuration_perceiver.py

@@ -65,7 +65,7 @@ class PerceiverConfig(PretrainedConfig):
         v_channels (`int`, *optional*):
             Dimension to project the values before applying attention in the cross-attention and self-attention layers
             of the encoder. Will default to preserving the dimension of the queries if not specified.
-        cross_attention_shape_for_attention (`str`, *optional*, defaults to `'kv'`):
+        cross_attention_shape_for_attention (`str`, *optional*, defaults to `"kv"`):
             Dimension to use when downsampling the queries and keys in the cross-attention layer of the encoder.
         self_attention_widening_factor (`int`, *optional*, defaults to 1):
             Dimension of the feed-forward layer in the cross-attention layer of the Transformer encoder.
@@ -89,7 +89,7 @@ class PerceiverConfig(PretrainedConfig):
             this to something large just in case (e.g., 512 or 1024 or 2048).
         image_size (`int`, *optional*, defaults to 56):
             Size of the images after preprocessing, for [`PerceiverForImageClassificationLearned`].
-        train_size (`List[int]`, *optional*, defaults to [368, 496]):
+        train_size (`List[int]`, *optional*, defaults to `[368, 496]`):
             Training size of the images for the optical flow model.
         num_frames (`int`, *optional*, defaults to 16):
             Number of video frames used for the multimodal autoencoding model.
@@ -97,11 +97,11 @@ class PerceiverConfig(PretrainedConfig):
             Number of audio samples per frame for the multimodal autoencoding model.
         samples_per_patch (`int`, *optional*, defaults to 16):
             Number of audio samples per patch when preprocessing the audio for the multimodal autoencoding model.
-        output_num_channels (`int`, *optional*, defaults to 512):
-            Number of output channels for each modalitiy decoder.
         output_shape (`List[int]`, *optional*, defaults to `[1, 16, 224, 224]`):
             Shape of the output (batch_size, num_frames, height, width) for the video decoder queries of the multimodal
             autoencoding model. This excludes the channel dimension.
+        output_num_channels (`int`, *optional*, defaults to 512):
+            Number of output channels for each modalitiy decoder.
 
     Example: